From this complete semantic map \(m^c\) , we create a training data tuple (\(m^p\) , \(U^a\) , \(U^o\) ) which consists of the partial semantic map, and the area and object potential functions (see columns 2 to 4 in Fig. REF ). The partial map \(m^p\)  is a subset of \(m^c\) , and serves as a proxy for the semantic map an embodied agent would observe while navigating in the 3D environment. It is computed as follows. We initialize an all-zeroes `exploration mask' \(m^e\)  with the same size as \(m^c\) . We then randomly pick two free-space locations on the \(m^c\)  and compute the shortest path between them using a classical planner [1]}. For each location \(x\)  on the shortest-path, we set a \(S \times S\)  square patch centered around \(x\)  to 1 in \(m^e\) , indicating that these parts of the map have been explored. We copy values from the \(m^c\)  to \(m^p\)  only for locations that are set to `explored' in \(m^e\) . The remaining locations in \(m^p\)  are left unexplored. The distances to each object from the frontiers in \(m^p\)  are obtained using shortest-path planning [1]}, [3]} on the complete map \(m^c\) . The area and object potential functions are computed as discussed in Sec. REF .
